Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years. However, the diversity of filters has not been fully utilized in the current research. This paper constructs a new effective network with diverse filter modules (DFMs) and squeeze-and-excitation modules (SEMs), which can better capture the embedding artifacts. As the essential parts, combining three different scale convolution filters, DFMs can process information diversely, and the SEMs can enhance the effective channels out from DFMs. The experiments presented that our CNN is effective against content-adaptive steganographic schemes with different payloads, such as S-UNIWARD and WOW algorithms. Moreover, some state-of-the-art methods are compared with our approach to demonstrate the outstanding performance.

中文翻译：

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通过不同的滤波器和压缩-激励卷积神经网络进行图像隐写分析

隐写分析是一种检测对象是否包含秘密消息的方法。随着深度学习的普及，使用卷积神经网络（CNN），隐写分析方案已成为近年来对抗隐写术的主要方法。但是，滤波器的多样性尚未在当前研究中得到充分利用。本文构建了一个新的有效网络，该网络具有各种过滤器模块（DFM）和挤压激励模块（SEM），可以更好地捕获嵌入的伪像。DFM作为必不可少的部分，结合了三个不同的比例卷积滤波器，可以多样化地处理信息，而SEM可以增强DFM的有效通道。实验表明，我们的CNN可以有效应对具有不同有效负载的内容自适应隐写方案，例如S-UNIWARD和WOW算法。